Optimal design of a hybrid energy plant by accounting for the cumulative energy demand

In this paper, the optimal design of a hybrid energy plant composed of a solar thermal collector, a photovoltaic panel, a combined heat and power system, an absorption chiller, an air source heat pump, a ground source heat pump and a thermal energy storage is studied. The size of each technology is optimized by applying a model implemented in Matlab® environment. The optimization goal is the minimization of the primary energy consumed throughout the life cycle of the hybrid energy plant by using a genetic algorithm. The primary energy consumed during the manufacturing phase of the hybrid energy plant is represented by the cumulative energy demand and is calculated by carrying out a cradle to gate life cycle assessment. The primary energy consumed during the operation phase is evaluated by simulating the system throughout one year. The cumulative energy demand of each system composing the hybrid energy plant is calculated as a function of the technology size. Therefore, the problem of life cycle assessment scaling of renewable and non-renewable energy systems is also taken into account in this paper. A tower located in the north of Italy is selected as a case study and two different approaches are evaluated. The first approach consists of solving the sizing optimization problem by minimizing the primary energy consumption only during the operation phase, while in the second approach the primary energy consumption is minimized throughout the life cycle of the plant by integrating the life cycle assessment into the optimization process. The results show that, if life cycle assessment is accounted for, the optimal hybrid energy plant configuration is different and a higher primary energy saving (approximately 12%) is achieved

Optimization of hybrid energy plants by accounting for life cycle energy demand

Un impianto energetico ibrido consiste in una combinazione di diversi sistemi energetici alimentati da diversi fonti energetiche, i quali quando vengono integrati, consistono di superare i limiti dei singoli sistemi. Diversi sistemi energetici possono essere integrati in un unico impianto ibrido a seconda della disponibilità delle diverse fonti energetiche. Diversi studi presenti in letteratura affermano che gli impianti energetici ibridi hanno la potenzialità di fornire energia con migliore qualità ed affidabilità rispetto ad un sistema alimentato da una singola fonte energetica. I benefici energetici ed ambientali degli impianti energetici ibridi destinati ad uso civile sono legati al dimensionamento e controllo di questi sistemi. In altre parole, i fattori fondamentali per un risparmio di energia e per la riduzione delle emissioni sono il dimensionamento ed il controllo ottimizzati dei vari sistemi che compongo l’impianto energetico ibrido. Inoltre, l’ottimizzazione di un impianto energetico ibrido deve basarsi sulla corrispondenza tra l’energia prodotta dai vari sistemi e la richiesta energetica dell’edificio. L’ottimizzazione degli impianti energetici ibridi viene solitamente condotta considerando gli impatti ambientali durante la vita utile. Tuttavia, questo approccio, che tiene conto solo dell’impatto ambientale, del costo o del consumo di energia primaria legato al funzionamento dell’impianto, può far sì che gli impatti ambientali legati alle altre fasi del ciclo di vita (i.e. la fase di costruzione e di smaltimento) non vengono presi in considerazione. Data la complessità legata al numero di variabili coinvolte, il fatto che le fonti di energia disponibili sono molteplici, la scelta dei sistemi di conversione dell’energia e l’integrazione del ciclo di vita in processi di ottimizzazione, la soluzione di tale problema richiede la disponibilità dei metodi e delle linee guida per l’ottimizzazione degli impianti energetici ibridi al fine di ottenere un risultato ottimale in termini di risparmio energetico e di conseguenza riduzione dell’impatto ambientale durante il ciclo di vita dell’impianto. Perciò, il lavoro di questa tesi di dottorato si concentra sullo sviluppo dei metodi e delle linee guida per l’ottimizzazione di impianti energetici ibridi minimizzando l’energia primaria consumata durante il ciclo di vita dell’impianto. Questo lavoro presenta un nuovo metodo, basato sulle tecniche di programmazione dinamica, per l’ottimizzazione di impianti energetici ibridi minimizzando il consumo di energia primaria durante il funzionamento. La metodologia sviluppata in questo lavoro estende l’uso del metodo di programmazione dinamica per risolvere dei problemi legati al dimensionamento e controllo ottimizzati di impianti complessi. Questo metodo è veloce, facile da implementare e tiene conto anche della non-linearità dei sistemi ibridi. Inoltre, questo lavoro affronta la valutazione del ciclo di vita di sistemi alimentati da fonti rinnovabili e non-rinnovabili destinati ad uso residenziale mediante un approccio “cradle-to-gate” applicato ai vari sistemi energetici. Inoltre, si affronta il problema del calcolo dell’inventario dei vari sistemi per diverse taglie e si illustrano i vari coefficienti usati per il calcolo dell’inventario in funzione della taglia. La procedura sviluppata consente di ottenere delle curve di impatto che possono essere usate per l’ottimizzazione dei sistemi energetici. Infine, viene sviluppata una metodologia per l’integrazione dell’analisi del ciclo di vita nel processo di ottimizzazione di impianti energetici ibridi. La metodologia viene applicata ad un caso studio, che consiste in un impianto energetico ibrido costituito da sistemi alimentati da energia rinnovabile e non-rinnovabile. L’ottimizzazione viene condotta minimizzando il consumo di energia primaria durante la fase di costruzione, trasporto e funzionamento dell’impianto.Hybrid energy plants may be a solution to overcome the limitations of a single source of energy, both based on renewable and non-renewable energy sources. A hybrid energy plant consists in a combination of two or more energy conversion systems which use different energy sources, that, when integrated, overcome the respective limitations. Several energy systems could be integrated in a hybrid energy plant depending on the availability of their primary energy resources. Hybrid energy plants have the potential to provide higher quality and better reliability of energy supply compared to a system based on a single source of energy. The promising energy and environmental benefits of hybrid energy plants for building applications are greatly dependent upon their design and operation strategy. In other words, the key factors for the achievement of an as high as possible primary energy saving and greenhouse gas emission reduction are the correct sizing and operation of the hybrid energy plant. Moreover, the optimization process of a hybrid energy plant must be based on the efficient match between building energy demand and supply. The optimal design of hybrid energy plants is commonly achieved by accounting for their environmental impacts during their useful life. However, this common approach, which only accounts for on-site environmental impacts, costs or primary energy consumption, may lead to burden shifting by ignoring the upstream life cycle of the hybrid energy plant. Given the complexity to deal with the number of variables involved, the multiple sources of energy that can be used, the choice of energy converters, the integration of life cycle assessment in system’s design and operation, procedures ad guidelines are needed for the solution of such complex problem, i.e. the optimization of hybrid energy plants in order to achieve an optimal result in term of primary energy saving and consequently environmental impacts reduction over the life cycle of the plant. For these reasons, the work of this thesis focuses on the development of original methods and procedures for the optimization of hybrid energy plants by accounting for the on-site and off-site energy consumption or environmental impacts calculated throughout the various stages of the life cycle of the energy plant. This work provides a new dynamic programming based optimization method to solve the optimization problem of hybrid energy plants by minimizing the on-site primary consumption. The proposed methodology extends the use of the dynamic programming method and attempts to apply it to solve both the sizing and operating optimization problems. Moreover, the presented method is fast, easy to implement and also addresses the nonlinearity associated with the characteristics of a hybrid energy plant. In addition, this work, investigates the life cycle assessment of renewable and non-renewable energy systems which can be employed for residential applications. For each system a cradle-to-gate life cycle assessment is carried out. The considered impact parameter is the cumulative energy demand. Furthermore, the problem of life cycle inventory scaling is addressed and appropriate scaling factors and their relevance for calculating environmental impacts are presented. The scaling procedure used in this work allows to obtain impact curves which can be used for optimization purposes. Finally, a general procedure for the integration of life cycle assessment into system’s design and optimization is developed. A case study consisting of a hybrid energy plant, which is composed of renewable and non-renewable energy systems, is considered to demonstrate the proposed approach. The optimization is carried out by taking into account the non-linear life cycle inventory scaling of energy systems and is conducted with the aim of minimizing the primary energy consumed during the manufacturing, transportation and operation phases

Optimal Management of the Energy Flows of Interconnected Residential Users

In recent years, residential users have begun to be equipped with micro-CHP (combined heat and power) generation technologies with the aim of decreasing primary energy consumption and reducing environmental impact. In these systems, the prime mover supplies both thermal and electrical energy, and an auxiliary boiler and the national electrical grid are employed as supplementary systems. In this paper, a simulation model, which accounts for component efficiency and energy balance, was developed to replicate the interaction between the users and the energy systems in order to minimize primary energy consumption. The simulation model identified the optimal operation strategy of two residential users by investigating different energy system configurations by means of a dynamic programming algorithm. The reference scenario was compared to three different scenarios by considering independent energy systems, shared thermal and electrical energy storage and also the shared prime mover. Such a comparison allowed the identification of the most suitable energy system configuration and optimized operation strategy. The results demonstrate that the optimized operation strategy smoothes the influence of the size of thermal and electrical energy storage. Moreover, the saving of primary energy consumption can be as high as 5.1%. The analysis of the economic feasibility reveals that the investment cost of the prime mover can be as high as 4000 €/kW

A diagnostic approach for fault detection and identification in district heating networks

District Heating Network (DHN) pipes can be affected by faults that endanger system reliability. Thus, this paper develops a novel modeling and diagnostic approach for the detection and identification of the most frequent faults that affect DHN pipes, i.e., water leakages, heat losses and pressure losses. The modeling approach exploits physics-based equations for calculating all DHN measurable variables, i.e., flow rate, temperature and pressure. The diagnostic approach detects and identifies pipe faults by coupling the modeling approach with an optimization algorithm. As a result, the diagnostic approach provides the health indices of each pipe of the DHN, which identify the faulty pipe, the fault type and its magnitude. The modeling approach proves to be extremely accurate since the Root Mean Square Error of the DHN variables is always lower than 0.02%. Furthermore, the modeling approach is exploited to infer general guidelines about the DHN health state to investigate the relationship between health indices and DHN characteristics. The novel diagnostic approach is verified by implanting six faults in the DHN of the campus of the University of Parma. All faults are correctly detected and identified, by also evaluating the exact fault magnitude

Analysis of a Multi-Generation Renewable Energy System with Hydrogen-Fueled Gas Turbine

Solar energy is considered one of the most affordable renewable resources for meeting current energy demands and mitigating environmental problems. However, the exploitation of solar energy is challenging because of both diurnal and seasonal variations. Power-to-hydrogen technologies can play a key role to counterbalance the variation of solar irradiance. Moreover, hydrogen-fueled gas turbines are considered promising technologies to decarbonize the electricity sector. To tackle these concerns, this paper presents a multigeneration energy system operated in island mode in which a hydrogen-fueled gas turbine is coupled with a solar photovoltaic plant, an electrolyzer, an absorption chiller, electric and thermal energy storage, as well as a hydrogen storage. Therefore, the energy system is 100% based on renewable energy. The sizes of the components are optimized by maximizing the exploitation of renewable energy sources, while the supply of electricity from the national grid must be null. Moreover, the effect of ambient conditions on the optimal sizing is also investigated by considering the thermal, cooling, and electrical energy demands of two case studies located in two different climatic zones. The paper demonstrates that the adoption of hydrogen-fueled gas turbines coupled with power-to-hydrogen technologies can effectively support the transition toward a clean energy supply. Moreover, this study provides a procedure for the optimal sizing of a multigeneration energy system fully based on solar energy, by also demonstrating that both photovoltaic (PV) panel area and hydrogen storage volume are feasible, if compared to the considered district layout

Sizing and Operation of a Hybrid Energy Plant Composed of Industrial Gas Turbines, Renewable Energy Systems, and Energy Storage Technologies

Hybrid energy plants (HEPs), which include both fossil fuel technologies and renewable energy systems, can provide an important step toward a sustainable energy supply. In fact, the hybridization of renewable energy systems with gas turbines (GTs), which are fed by fossil fuels allows an acceptable compromise, so that high fossil fuel efficiency and high share of renewables can be potentially achieved. Moreover, electrical and thermal energy storage systems increase the flexibility of the energy plant and effectively manage the variability of energy production and demand. This paper investigates the optimal sizing of a HEP, which combines an industrial GT, renewable energy systems, and energy storage technologies. The considered renewable energy system is a photovoltaic system (PV), while the energy storage technologies are electrical energy storage and thermal energy storage. Moreover, a compression chiller and a gas boiler (GB) are also considered. For this purpose, the load profiles of electricity, heating, and cooling during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy). The sizing optimization problem of the different technologies composing the HEP is solved by using a genetic algorithm, with the goal of minimizing the primary energy consumption (PEC). Moreover, different operation strategies are analyzed and compared so that plant operation is also optimized. The results demonstrate that the optimal sizing of the HEP, coupled with the optimized operation strategy, allows high average cogeneration efficiency (up to 84%), thus minimizing PEC

Optimal design and energy management of a renewable energy plant with seasonal energy storage

The exploitation of fossil fuels is undoubtedly responsible of environmental problems such as global warming and sea level rise. Unlike energy plants based on fossil fuels, energy plants based on renewable energy sources may be sustainable and reduce greenhouse gas emissions. However, they are unpredictable because of the intermittent nature of environmental conditions. For this reason, energy storage technologies are needed to meet peak energy demands thanks to the stored energy. Moreover, the renewable energy systems composing the plant must be optimally designed and operated. Therefore, this paper investigates the challenge of the optimal design and energy management of a grid connected renewable energy plant composed of a solar thermal collector, photovoltaic system, ground source heat pump, battery, one short-term thermal energy storage and one seasonal thermal energy storage. To this aim, this paper develops a methodology based on a genetic algorithm that optimally designs a 100% renewable energy plant with the aim of minimizing the electrical energy taken from the grid. The load profiles of thermal, cooling and electrical energy during a whole year are taken into account for the case study of the Campus of the University of Parma (Italy)

Detection and identification of faults in a District Heating Network

District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. The novel diagnostic approach proves to correctly detect and identify all simulated faults, by also correctly estimating their magnitude even in the most challenging scenarios